Load packages

Load RL URD object

We removed presumably non-r1 derived cells.

urd_RL <- readRDS(file="./data/urd_RL.rds")
non_r1.cell.group <- c("Tlx3","Isl1","Is","10")
cell.removed <- cellsInCluster(urd_RL, "ident", non_r1.cell.group)
length(cell.removed)
[1] 136
cells.keep <- setdiff(colnames(urd@logupx.data), cell.removed)
length(cells.keep)
[1] 1818
urd.trimmed <- urdSubset(urd_RL, cells.keep=cells.keep)
plotDim(urd.trimmed, "ident",label.clusters = T, legend = F)

saveRDS(urd.trimmed, "./data/urd_trimmed.rds")

URD analysis

This step will take some time. We will skip this one and load the previously saved object.

Load previously saved objects

urd <- readRDS("./data/urd_trimmed.rds")
# Define C1 cells as root cells 
plotDimHighlight(urd, "ident", "C1", legend=F)

root.cells <- cellsInCluster(urd, "ident", c("C1"))
dm <- readRDS("./data/dm.s16_n75.rds")
flood.result <- readRDS(file="./data/flood-dm.s16-random200.rds")
urd <- importDM(urd, dm)
urd <- floodPseudotimeProcess(urd, flood.result, floods.name="pseudotime", max.frac.NA=0.4, pseudotime.fun=mean, stability.div=20)
### Inspect pseudotime
p0 <- pseudotimePlotStabilityOverall(urd) + 
  geom_hline(yintercept = 2.5, linetype="dashed", color = "red")
p1 <- plotDim(urd, "ident", legend=F, plot.title="Putative tip cells", alpha=1,label.clusters = T)
p2 <- plotDim(urd, "pseudotime", plot.title = "Pseudotime",legend=F)
print(plot_grid(p0,p1,p2, ncol = 3))

# Create a data.frame that includes pseudotime and stage information
gg.data <- cbind(urd@pseudotime, urd@meta[rownames(urd@pseudotime),])
gg.data$tip.Name <- urd@group.ids[rownames(gg.data),]$ident
# Plot pseudotime
ggplot(gg.data, aes(x=pseudotime, color=tip.Name, fill=tip.Name)) + geom_density(alpha=0.4) + theme_bw()+theme(legend.position="none")+
  facet_wrap( ~ tip.Name, ncol=6)

ggplot(gg.data, aes(x=pseudotime, color=tip.Name, fill=tip.Name)) + geom_density(alpha=0.4) + theme_bw()+theme(legend.position="none")

# pass the cluster to claster name
urd@group.ids$tip.name <- NULL
urd@group.ids$tip.name[urd@group.ids$ident== "mCN"] <- "mCN"
urd@group.ids$tip.name[urd@group.ids$ident== "lCN"] <- "lCN"
urd@group.ids$tip.name[urd@group.ids$ident== "GC"] <- "GC"
urd@group.ids$class <- NULL
urd@group.ids$class[!is.na(urd@group.ids$tip.name)] <- "Tip"
urd@group.ids$tip.num <- NA
urd@group.ids$tip.num[urd@group.ids$ident== "mCN"] <- "3"
urd@group.ids$tip.num[urd@group.ids$ident== "lCN"] <- "2"
urd@group.ids$tip.num[urd@group.ids$ident== "GC"] <- "1"
saveRDS(urd, file="./data/urd.rds")

Biased random walks from each tip

# Define parameters of logistic function to bias transition probabilities
diffusion.logistic <- pseudotimeDetermineLogistic(urd, "pseudotime", optimal.cells.forward=20, max.cells.back=40, pseudotime.direction="<", do.plot=T, print.values=T)
[1] "Mean pseudotime back (~40 cells) 0.0139819579440117"
[1] "Chance of accepted move to equal pseudotime is 0.82442639407355"
[1] "Mean pseudotime forward (~20 cells) -0.00695152465444183"

biased.tm <- pseudotimeWeightTransitionMatrix(urd, pseudotime = "pseudotime", logistic.params = diffusion.logistic, pseudotime.direction = "<")
# We ran this in the HPC cluster
# # # Simulate the biased random walks from each tip
# walks <- simulateRandomWalksFromTips(urd, tip.group.id = "tip.num", root.cells = root.cells,
#                                      transition.matrix = biased.tm, n.per.tip = 25000, root.visits = 1,
#                                      max.steps = 5000, verbose = T)
# saveRDS(walks, file = "./data/walks_n25000.rds")
# retrieve previously calculated random walks results
walks <- readRDS("./data/walks_n25000.rds")
# Process the biased random walks into visitation frequencies
urd <- processRandomWalksFromTips(urd, walks, verbose = T)
[1] "2019-01-22 13:51:08 - Processing walks from tip 1"
[1] "Calculating pseudotime with 2500 walks."
[1] "Calculating pseudotime with 5000 walks."
[1] "Calculating pseudotime with 7500 walks."
[1] "Calculating pseudotime with 10000 walks."
[1] "Calculating pseudotime with 12500 walks."
[1] "Calculating pseudotime with 15000 walks."
[1] "Calculating pseudotime with 17500 walks."
[1] "Calculating pseudotime with 20000 walks."
[1] "Calculating pseudotime with 22500 walks."
[1] "Calculating pseudotime with 25000 walks."
[1] "2019-01-22 13:51:13 - Processing walks from tip 2"
[1] "Calculating pseudotime with 2500 walks."
[1] "Calculating pseudotime with 5000 walks."
[1] "Calculating pseudotime with 7500 walks."
[1] "Calculating pseudotime with 10000 walks."
[1] "Calculating pseudotime with 12500 walks."
[1] "Calculating pseudotime with 15000 walks."
[1] "Calculating pseudotime with 17500 walks."
[1] "Calculating pseudotime with 20000 walks."
[1] "Calculating pseudotime with 22500 walks."
[1] "Calculating pseudotime with 25000 walks."
[1] "2019-01-22 13:51:19 - Processing walks from tip 3"
[1] "Calculating pseudotime with 2500 walks."
[1] "Calculating pseudotime with 5000 walks."
[1] "Calculating pseudotime with 7500 walks."
[1] "Calculating pseudotime with 10000 walks."
[1] "Calculating pseudotime with 12500 walks."
[1] "Calculating pseudotime with 15000 walks."
[1] "Calculating pseudotime with 17500 walks."
[1] "Calculating pseudotime with 20000 walks."
[1] "Calculating pseudotime with 22500 walks."
[1] "Calculating pseudotime with 25000 walks."
saveRDS(urd, file="./data/urd.rds")
gridExtra::grid.arrange(grobs=list(
  plotDim(urd, "tip.name", plot.title="Cells in each tip"),
  plotDim(urd, "visitfreq.log.1", transitions.plot=10000, plot.title="visitfreq_GC"),
  plotDim(urd, "visitfreq.log.2", transitions.plot=10000, plot.title="visitfreq_mCN"),
  plotDim(urd, "visitfreq.log.3", transitions.plot=10000, plot.title="visitfreq_lCN")
))

Build trajectory tree

urd.tree <- loadTipCells(urd, "tip.num") 
tip.id <- setdiff(unique(urd.tree@group.ids$tip.num),c(NA))
urd.tree <- buildTree(urd.tree, pseudotime = "pseudotime", tips.use = tip.id, divergence.method = "ks", 
                      cells.per.pseudotime.bin = 30, bins.per.pseudotime.window = 8, minimum.visits = 10,
                      visit.threshold = 0.7, use.only.original.tips = T,
                      save.all.breakpoint.info = T, p.thresh=0.05)
[1] "Calculating divergence between 1 and 3 (Pseudotime 0 to 0.591)"
[1] "Calculating divergence between 1 and 2 (Pseudotime 0 to 0.591)"
[1] "Calculating divergence between 3 and 2 (Pseudotime 0 to 0.62)"
[1] "Joining segments 3 and 2 at pseudotime 0.573 to create segment 4"
[1] "Calculating divergence between 1 and 4 (Pseudotime 0 to 0.573)"
[1] "Joining segments 1 and 4 at pseudotime 0.462 to create segment 5"
[1] "Assigning cells to segments."
73 cells were not visited by a branch that exists at their pseudotime and were not assigned.
[1] "Collapsing short segments."
[1] "Removing singleton segments."
[1] "Reassigning cells to segments."
73 cells were not visited by a branch that exists at their pseudotime and were not assigned.
[1] "Assigning cells to nodes."
[1] "Laying out tree."
[1] "Adding cells to tree."
# Name the segments
urd.tree <- nameSegments(urd.tree, segments= tip.id, 
                         segment.names = c("GC", "mCN","lCN"), 
                         short.names = tip.id)

Plot results

plotTree(urd.tree, "segment", title="URD tree segment", label.segments = T)

plotTree(urd.tree, "ident", title="URD cluster ID", label.segments = T)

Plot gene expression over the trajectory

gridExtra::grid.arrange(grobs=list(
  plotTree(urd.tree, "Lmx1a", title="mCN marker_Lmx1a"),
  plotTree(urd.tree, "Olig2", title="lCN marker_Olig2"),
  plotTree(urd.tree, "Atoh1", title="GC marker_Atoh1"),
  plotTree(urd.tree, "Wnt1", title="C1 marker_Wnt1")
))

saveRDS(urd.tree, file = "./data/urdTree.rds")

Examine gene expression cascades

urd.tree <- readRDS("./data/urdTree.rds")

colnames(urd.tree@meta) <- c("n.Genes","n.Trans","orig.ident","percent.mito","tree.ident","clust")

tips.to.run <- setdiff(as.character(urd.tree@tree$segment.names), NA)
genes.use <- NULL # Calculate for all genes

# Calculate the markers of each other population.
gene.markers <- list()
markers.sum <- NULL
for (tip in tips.to.run) {
  print(paste0(Sys.time(), ": ", tip))
  markers <- aucprTestAlongTree(urd.tree, pseudotime = "pseudotime", tips = tip, log.effect.size = 0.4,
                                auc.factor = 1.25, max.auc.threshold = 0.85, frac.must.express = 0.1,
                                frac.min.diff = 0.1, genes.use = genes.use, root = NULL,
                                segs.to.skip = NULL, only.return.global = F, must.beat.sibs = 0.6,
                                report.debug = T)
  gene.markers[[tip]] <- markers
}
saveRDS(gene.markers, "./data/geneMarkers.rds")

# Separate actual marker lists from the stats lists
gene.markers.de <- lapply(gene.markers, function(x) x[[1]])
gene.markers.stats <- lapply(gene.markers, function(x) x[[2]])
names(gene.markers.de) <- names(gene.markers)
names(gene.markers.stats) <- names(gene.markers)

# Compile all comparison stats into a single table
all.de.stats <- do.call("rbind", gene.markers.stats)
all.de.stats$tip <- substr(rownames(all.de.stats),1,nchar(rownames(all.de.stats))-2)

# Do a few plots
p1 <- ggplot(all.de.stats, aes(x=pt.1.mean, y=pt.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Pseudotime (Group 1)", y="Mean Pseudotime (Group 2)")
p2 <- ggplot(all.de.stats, aes(x=genes.1.mean, y=genes.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Detected Genes (Group 1)", y="Mean Detected Genes (Group 2)")
p3 <- ggplot(all.de.stats, aes(x=trans.1.mean, y=trans.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Transcripts (Group 1)", y="Mean Transcripts (Group 2)")

plot_grid(p1,p2,p3, ncol = 3)


# Create a fold to hold the results
path <- ".data/"
dir.create(paste0(path,"cascades"))

# Generate impulse fits
gene.cascades <- lapply(tips.to.run, function(tip) {
  print(paste0(Sys.time(), ": Impulse Fit ", tip))
  seg.cells <- cellsAlongLineage(urd.tree, tip, remove.root=F)
  casc <- geneCascadeProcess(object = urd.tree, pseudotime='pseudotime', cells = seg.cells, genes= rownames(gene.markers.de[[tip]]), 
                             moving.window=3, cells.per.window=10, limit.single.sigmoid.slopes = "on", verbose = T)
  tip.file.name <- gsub("/", "_", tip)
  saveRDS(casc, file=paste0(path, "cascades/casc_", tip.file.name, ".rds"))
  return(casc)
})
names(gene.cascades) <- tips.to.run

# Make a heatmap of every cascade in a single PDF.
for (tip in tips.to.run) {
  gene.num <- nrow(gene.cascades[[tip]]$scaled.expression)
  geneCascadeHeatmap(cascade=gene.cascades[[tip]], title = tip, row.font.size = 0.06*gene.num)
}

# =====repeat with TF genes only ============
# Identify DE genes of each other population (restricted to transcription factors only)
mGenes <- readRDS("/Volumes/jali/Genome/Annotation1.rds")
TF.use <- intersect(rownames(urd@logupx.data),mGenes[which(mGenes$Type > "nonTF"), "mgi_symbol"]);length(TF.use)

TF.markers <- list()
for (tipn in 1:length(tips.to.run)) {
  tip <- tips.to.run[tipn]
  print(paste0(Sys.time(), ": ", tip))
  markers <- aucprTestAlongTree(urd.tree, pseudotime = "pseudotime", tips = tip, log.effect.size = 0.25,
                                auc.factor = 1.25, max.auc.threshold = 0.85, frac.must.express = 0.1,
                                frac.min.diff = 0.1, genes.use = TF.use, root = NULL,
                                segs.to.skip = NULL, only.return.global = F, must.beat.sibs = 0.6,
                                report.debug = T)
  TF.markers[[tip]] <- markers
}
saveRDS(TF.markers, "./data/geneMarkers_TF.rds")

# ====== Generate impulse fits for DE transcription factors ====== 
TF.markers.de <- lapply(TF.markers, function(x) x[[1]])
names(TF.markers.de) <- names(TF.markers)

TF.cascades <- lapply(tips.to.run, function(tip) {
  print(paste0(Sys.time(), ": Impulse Fit ", tip))
  seg.cells <- cellsAlongLineage(urd.tree, tip, remove.root=F)
  casc <- geneCascadeProcess(object = urd.tree, pseudotime='pseudotime', cells = seg.cells, genes= rownames(TF.markers.de[[tip]]), 
                             # background.genes = sample(setdiff(rownames(urd.tree@logupx.data),urd.tree@var.genes), 1000),
                             moving.window=3, cells.per.window=10, limit.single.sigmoid.slopes = "on", verbose = T)
  return(casc)
})
names(TF.cascades) <- tips.to.run
saveRDS(TF.cascades, "./data/TF.cascades.rds")

# Make a heatmap of every cascade in a single PDF.
pdf(file=paste0(path,"cascades/cascades_TF.pdf"), width=5, height=6)
for (tip in tips.to.run) {
  gene.num <- nrow(TF.cascades[[tip]]$scaled.expression)
  geneCascadeHeatmap(cascade=TF.cascades[[tip]], title = tip, row.font.size = 0.6*gene.num)
}
dev.off()

# create a table of markers genes with timing
markers.sum <- NULL
for (tip in tips.to.run) {
  res <- gene.markers.de[[tip]]
  res$tip <- tip
  cascade=gene.cascades[[tip]]
  
  # Correct for NA timings
  timing <- cascade$timing
  timing[intersect(which(is.na(timing$time.on)), which(is.infinite(timing$time.off))), "time.on"] <- Inf
  res <- cbind(res,timing)
  gene.order <- order(timing$time.on, timing$time.off, na.last=F)
  res <- res[gene.order,]
  res$gene <- rownames(res)
  res$TF <- mGenes$Type[match(res$gene,mGenes$mgi_symbol)]
  res$description <- mGenes$description[match(res$gene,mGenes$mgi_symbol)]
  markers.sum <- rbind(markers.sum, res)
}
head(markers.sum)


### ====== Identify markers of each lineage ====== 
markers.sum <- NULL
tip <- "GC"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)

# Determine which genes are also global markers
cells <- cellsInCluster(urd.tree, "segment", c("1"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, cells.2 = NULL, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), factor = 2.5, max.auc = Inf) # lower the stringency by reducing the factor 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# find marker of caudal thalamus
tip <- "lCN"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)
cells <- cellsInCluster(urd.tree, "segment", c("2"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), factor = 2.5, max.auc = Inf) 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# find marker of caudal thalamus
tip <- "mCN"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)
cells <- cellsInCluster(urd.tree, "segment", c("3"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, cells.2 = NULL, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), 
                                factor = 2.5, max.auc = Inf) # lower the stringency by reducing the factor 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# openxlsx::write.xlsx(markers.sum, file = "./tables/segmantMarkers_all.xlsx")
openxlsx::write.xlsx(markers.sum, file = "./tables/lineageMarkers_all.xlsx")
---
title: "Cell clustering of the RL lineage"
output:
  html_document:
    df_print: paged
  html_notebook: default
linestretch: 0.5
---
\fontsize{8}{18}

# Load packages
```{r load-packages, include=FALSE, message=F, warning=F}
library(URD);library(cowplot)
```

# Load RL URD object
We removed presumably non-r1 derived cells. 
```{r fig.width=3, fig.asp=1}
urd_RL <- readRDS(file="./data/urd_RL.rds")

non_r1.cell.group <- c("Tlx3","Isl1","Is","10")
cell.removed <- cellsInCluster(urd_RL, "ident", non_r1.cell.group)
length(cell.removed)
cells.keep <- setdiff(colnames(urd@logupx.data), cell.removed)
length(cells.keep)

urd.trimmed <- urdSubset(urd_RL, cells.keep=cells.keep)
plotDim(urd.trimmed, "ident",label.clusters = T, legend = F)
```

```{r}
saveRDS(urd.trimmed, "./data/urd_trimmed.rds")
```

# URD analysis
This step will take some time. We will skip this one and load the previously saved object.
```{r}
urd <- readRDS("./data/urd_trimmed.rds")

# estimate the knn value using sqrt(n.cells)
sqrt(ncol(urd@logupx.data))

#Test different sigma for diffusion map
for (s in c(8:16)) {
  urd <- calcDM(urd, knn = 75, sigma.use = s)
  dm <- urd@dm
  
  pdf(paste0("./figures/DMarray_s",s,"_n75.pdf"), h= 10, w = 10)
  plotDimArray(object = urd, reduction.use = "dm", dims.to.plot = 1:18, label="ident", plot.title="",
               outer.title=paste0("Cluster - Diffusion Map sigma = ",s,", k = 75"), legend=F, alpha=0.7)
  dev.off()
  saveRDS(dm, file = paste0("./data/dm.s",s,"_n75.rds"))
}

dm <- readRDS("./data/dm.s16_n75.rds")
urd <- importDM(urd, dm)

# Define C1 cells as root cells 
plotDimHighlight(urd, "ident", "C1", legend=F)
root.cells <- cellsInCluster(urd, "ident", c("C1"))


flood.result <- floodPseudotime(urd, root.cells=root.cells, n=200, minimum.cells.flooded=2, verbose=T)
saveRDS(flood.result, file="./data/flood-dm.s16-random200.rds")
```


# Load previously saved objects
```{r}
urd <- readRDS("./data/urd_trimmed.rds")
# Define C1 cells as root cells 
plotDimHighlight(urd, "ident", "C1", legend=F)
root.cells <- cellsInCluster(urd, "ident", c("C1"))

dm <- readRDS("./data/dm.s16_n75.rds")
flood.result <- readRDS(file="./data/flood-dm.s16-random200.rds")

urd <- importDM(urd, dm)
urd <- floodPseudotimeProcess(urd, flood.result, floods.name="pseudotime", max.frac.NA=0.4, pseudotime.fun=mean, stability.div=20)

### Inspect pseudotime
p0 <- pseudotimePlotStabilityOverall(urd) + 
  geom_hline(yintercept = 2.5, linetype="dashed", color = "red")
p1 <- plotDim(urd, "ident", legend=F, plot.title="Putative tip cells", alpha=1,label.clusters = T)
p2 <- plotDim(urd, "pseudotime", plot.title = "Pseudotime",legend=F)

print(plot_grid(p0,p1,p2, ncol = 3))

# Create a data.frame that includes pseudotime and stage information
gg.data <- cbind(urd@pseudotime, urd@meta[rownames(urd@pseudotime),])
gg.data$tip.Name <- urd@group.ids[rownames(gg.data),]$ident

# Plot pseudotime
ggplot(gg.data, aes(x=pseudotime, color=tip.Name, fill=tip.Name)) + geom_density(alpha=0.4) + theme_bw()+theme(legend.position="none")+
  facet_wrap( ~ tip.Name, ncol=6)
ggplot(gg.data, aes(x=pseudotime, color=tip.Name, fill=tip.Name)) + geom_density(alpha=0.4) + theme_bw()+theme(legend.position="none")
```

```{r}
# pass the cluster to claster name
urd@group.ids$tip.name <- NULL
urd@group.ids$tip.name[urd@group.ids$ident== "mCN"] <- "mCN"
urd@group.ids$tip.name[urd@group.ids$ident== "lCN"] <- "lCN"
urd@group.ids$tip.name[urd@group.ids$ident== "GC"] <- "GC"

urd@group.ids$class <- NULL
urd@group.ids$class[!is.na(urd@group.ids$tip.name)] <- "Tip"

urd@group.ids$tip.num <- NA
urd@group.ids$tip.num[urd@group.ids$ident== "mCN"] <- "3"
urd@group.ids$tip.num[urd@group.ids$ident== "lCN"] <- "2"
urd@group.ids$tip.num[urd@group.ids$ident== "GC"] <- "1"
```

```{r}
saveRDS(urd, file="./data/urd.rds")
```

# Biased random walks from each tip 
```{r}
# Define parameters of logistic function to bias transition probabilities
diffusion.logistic <- pseudotimeDetermineLogistic(urd, "pseudotime", optimal.cells.forward=20, max.cells.back=40, pseudotime.direction="<", do.plot=T, print.values=T)

biased.tm <- pseudotimeWeightTransitionMatrix(urd, pseudotime = "pseudotime", logistic.params = diffusion.logistic, pseudotime.direction = "<")

# We ran this in the HPC cluster
# # # Simulate the biased random walks from each tip
# walks <- simulateRandomWalksFromTips(urd, tip.group.id = "tip.num", root.cells = root.cells,
#                                      transition.matrix = biased.tm, n.per.tip = 25000, root.visits = 1,
#                                      max.steps = 5000, verbose = T)
# saveRDS(walks, file = "./data/walks_n25000.rds")
```


```{r}
# retrieve previously calculated random walks results
walks <- readRDS("./data/walks_n25000.rds")

# Process the biased random walks into visitation frequencies
urd <- processRandomWalksFromTips(urd, walks, verbose = T)
saveRDS(urd, file="./data/urd.rds")

gridExtra::grid.arrange(grobs=list(
  plotDim(urd, "tip.name", plot.title="Cells in each tip"),
  plotDim(urd, "visitfreq.log.1", transitions.plot=10000, plot.title="visitfreq_GC"),
  plotDim(urd, "visitfreq.log.2", transitions.plot=10000, plot.title="visitfreq_mCN"),
  plotDim(urd, "visitfreq.log.3", transitions.plot=10000, plot.title="visitfreq_lCN")
))
```

# Build trajectory tree
```{r}
urd.tree <- loadTipCells(urd, "tip.num") 
tip.id <- setdiff(unique(urd.tree@group.ids$tip.num),c(NA))
urd.tree <- buildTree(urd.tree, pseudotime = "pseudotime", tips.use = tip.id, divergence.method = "ks", 
                      cells.per.pseudotime.bin = 30, bins.per.pseudotime.window = 8, minimum.visits = 10,
                      visit.threshold = 0.7, use.only.original.tips = T,
                      save.all.breakpoint.info = T, p.thresh=0.05)
# Name the segments
urd.tree <- nameSegments(urd.tree, segments= tip.id, 
                         segment.names = c("GC", "mCN","lCN"), 
                         short.names = tip.id)
```

# Plot results
```{r}
plotTree(urd.tree, "segment", title="URD tree segment", label.segments = T)
plotTree(urd.tree, "ident", title="URD cluster ID", label.segments = T)
```

# Plot gene expression over the trajectory
```{r}
gridExtra::grid.arrange(grobs=list(
  plotTree(urd.tree, "Lmx1a", title="mCN marker_Lmx1a"),
  plotTree(urd.tree, "Olig2", title="lCN marker_Olig2"),
  plotTree(urd.tree, "Atoh1", title="GC marker_Atoh1"),
  plotTree(urd.tree, "Wnt1", title="C1 marker_Wnt1")
))
```

```{r}
saveRDS(urd.tree, file = "./data/urdTree.rds")
```

# Examine gene expression cascades 
```{r}
urd.tree <- readRDS("./data/urdTree.rds")

colnames(urd.tree@meta) <- c("n.Genes","n.Trans","orig.ident","percent.mito","tree.ident","clust")

tips.to.run <- setdiff(as.character(urd.tree@tree$segment.names), NA)
genes.use <- NULL # Calculate for all genes

# Calculate the markers of each other population.
gene.markers <- list()
markers.sum <- NULL
for (tip in tips.to.run) {
  print(paste0(Sys.time(), ": ", tip))
  markers <- aucprTestAlongTree(urd.tree, pseudotime = "pseudotime", tips = tip, log.effect.size = 0.4,
                                auc.factor = 1.25, max.auc.threshold = 0.85, frac.must.express = 0.1,
                                frac.min.diff = 0.1, genes.use = genes.use, root = NULL,
                                segs.to.skip = NULL, only.return.global = F, must.beat.sibs = 0.6,
                                report.debug = T)
  gene.markers[[tip]] <- markers
}
saveRDS(gene.markers, "./data/geneMarkers.rds")

# Separate actual marker lists from the stats lists
gene.markers.de <- lapply(gene.markers, function(x) x[[1]])
gene.markers.stats <- lapply(gene.markers, function(x) x[[2]])
names(gene.markers.de) <- names(gene.markers)
names(gene.markers.stats) <- names(gene.markers)

# Compile all comparison stats into a single table
all.de.stats <- do.call("rbind", gene.markers.stats)
all.de.stats$tip <- substr(rownames(all.de.stats),1,nchar(rownames(all.de.stats))-2)

# Do a few plots
p1 <- ggplot(all.de.stats, aes(x=pt.1.mean, y=pt.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Pseudotime (Group 1)", y="Mean Pseudotime (Group 2)")
p2 <- ggplot(all.de.stats, aes(x=genes.1.mean, y=genes.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Detected Genes (Group 1)", y="Mean Detected Genes (Group 2)")
p3 <- ggplot(all.de.stats, aes(x=trans.1.mean, y=trans.2.mean)) + geom_point() + theme_bw() + geom_abline(slope = 1, intercept=0, col='red', lty=2) + labs(x="Mean Transcripts (Group 1)", y="Mean Transcripts (Group 2)")

plot_grid(p1,p2,p3, ncol = 3)


# Create a fold to hold the results
path <- ".data/"
dir.create(paste0(path,"cascades"))

# Generate impulse fits
gene.cascades <- lapply(tips.to.run, function(tip) {
  print(paste0(Sys.time(), ": Impulse Fit ", tip))
  seg.cells <- cellsAlongLineage(urd.tree, tip, remove.root=F)
  casc <- geneCascadeProcess(object = urd.tree, pseudotime='pseudotime', cells = seg.cells, genes= rownames(gene.markers.de[[tip]]), 
                             moving.window=3, cells.per.window=10, limit.single.sigmoid.slopes = "on", verbose = T)
  tip.file.name <- gsub("/", "_", tip)
  saveRDS(casc, file=paste0(path, "cascades/casc_", tip.file.name, ".rds"))
  return(casc)
})
names(gene.cascades) <- tips.to.run

# Make a heatmap of every cascade in a single PDF.
for (tip in tips.to.run) {
  gene.num <- nrow(gene.cascades[[tip]]$scaled.expression)
  geneCascadeHeatmap(cascade=gene.cascades[[tip]], title = tip, row.font.size = 0.06*gene.num)
}

# =====repeat with TF genes only ============
# Identify DE genes of each other population (restricted to transcription factors only)
mGenes <- readRDS("/Volumes/jali/Genome/Annotation1.rds")
TF.use <- intersect(rownames(urd@logupx.data),mGenes[which(mGenes$Type > "nonTF"), "mgi_symbol"]);length(TF.use)

TF.markers <- list()
for (tipn in 1:length(tips.to.run)) {
  tip <- tips.to.run[tipn]
  print(paste0(Sys.time(), ": ", tip))
  markers <- aucprTestAlongTree(urd.tree, pseudotime = "pseudotime", tips = tip, log.effect.size = 0.25,
                                auc.factor = 1.25, max.auc.threshold = 0.85, frac.must.express = 0.1,
                                frac.min.diff = 0.1, genes.use = TF.use, root = NULL,
                                segs.to.skip = NULL, only.return.global = F, must.beat.sibs = 0.6,
                                report.debug = T)
  TF.markers[[tip]] <- markers
}
saveRDS(TF.markers, "./data/geneMarkers_TF.rds")

# ====== Generate impulse fits for DE transcription factors ====== 
TF.markers.de <- lapply(TF.markers, function(x) x[[1]])
names(TF.markers.de) <- names(TF.markers)

TF.cascades <- lapply(tips.to.run, function(tip) {
  print(paste0(Sys.time(), ": Impulse Fit ", tip))
  seg.cells <- cellsAlongLineage(urd.tree, tip, remove.root=F)
  casc <- geneCascadeProcess(object = urd.tree, pseudotime='pseudotime', cells = seg.cells, genes= rownames(TF.markers.de[[tip]]), 
                             # background.genes = sample(setdiff(rownames(urd.tree@logupx.data),urd.tree@var.genes), 1000),
                             moving.window=3, cells.per.window=10, limit.single.sigmoid.slopes = "on", verbose = T)
  return(casc)
})
names(TF.cascades) <- tips.to.run
saveRDS(TF.cascades, "./data/TF.cascades.rds")

# Make a heatmap of every cascade in a single PDF.
pdf(file=paste0(path,"cascades/cascades_TF.pdf"), width=5, height=6)
for (tip in tips.to.run) {
  gene.num <- nrow(TF.cascades[[tip]]$scaled.expression)
  geneCascadeHeatmap(cascade=TF.cascades[[tip]], title = tip, row.font.size = 0.6*gene.num)
}
dev.off()

# create a table of markers genes with timing
markers.sum <- NULL
for (tip in tips.to.run) {
  res <- gene.markers.de[[tip]]
  res$tip <- tip
  cascade=gene.cascades[[tip]]
  
  # Correct for NA timings
  timing <- cascade$timing
  timing[intersect(which(is.na(timing$time.on)), which(is.infinite(timing$time.off))), "time.on"] <- Inf
  res <- cbind(res,timing)
  gene.order <- order(timing$time.on, timing$time.off, na.last=F)
  res <- res[gene.order,]
  res$gene <- rownames(res)
  res$TF <- mGenes$Type[match(res$gene,mGenes$mgi_symbol)]
  res$description <- mGenes$description[match(res$gene,mGenes$mgi_symbol)]
  markers.sum <- rbind(markers.sum, res)
}
head(markers.sum)


### ====== Identify markers of each lineage ====== 
markers.sum <- NULL
tip <- "GC"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)

# Determine which genes are also global markers
cells <- cellsInCluster(urd.tree, "segment", c("1"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, cells.2 = NULL, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), factor = 2.5, max.auc = Inf) # lower the stringency by reducing the factor 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# find marker of caudal thalamus
tip <- "lCN"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)
cells <- cellsInCluster(urd.tree, "segment", c("2"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), factor = 2.5, max.auc = Inf) 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# find marker of caudal thalamus
tip <- "mCN"
tip.file.name <- gsub("/", "_", tip)
cascade <- readRDS(file = paste0("./cascades/casc_", tip.file.name, ".rds"))
markers <- rownames(cascade$scaled.expression)
cells <- cellsInCluster(urd.tree, "segment", c("3"))
markers.global <- markersAUCPR(urd.tree, cells.1 = cells, cells.2 = NULL, genes.use = markers, clustering = "segment")
marker.thresh <- aucprThreshold(cells.1 = cells, cells.2 = setdiff(unlist(urd.tree@tree$cells.in.segment), cells), 
                                factor = 2.5, max.auc = Inf) # lower the stringency by reducing the factor 
de.markers <- markers.global[markers.global$AUCPR >= marker.thresh,];nrow(de.markers)
de.markers$tip <- tip
de.markers$gene <- rownames(de.markers)
de.markers$TF <- mGenes$Type[match(de.markers$gene,mGenes$mgi_symbol)]
de.markers$description <- mGenes$description[match(de.markers$gene,mGenes$mgi_symbol)]
markers.sum <- rbind(markers.sum, de.markers)

# openxlsx::write.xlsx(markers.sum, file = "./tables/segmantMarkers_all.xlsx")
openxlsx::write.xlsx(markers.sum, file = "./tables/lineageMarkers_all.xlsx")
```

# Print session information
```{r}
sessionInfo()
```

